The invention relates to a method and a device for the inspection of surfaces of an examined object, where the surface is illuminated by means of an illuminating unit and captured by at least one area image sensor. The area image sensor may, in particular, be an area scan sensor also called a matrix camera, where an image detail is associated with one pixel of the area scan camera, where one pixel of the picture taken assumes a value which is characteristic of the image detail taken. The values may, in particular, be intensity values, wherein the picture may be captured in a monochromatic manner (for example as a grey value image, an infrared image or a monochrome image) as well as a in a polychromatic manner (color picture). The color is defined by the intensity values of the color sensors associated with the respective pixel of the matrix camera or the area image sensor, e.g. red, green, blue, infrared and/or similar wavelength-selective color sensors. According to the invention the area image sensor or the matrix camera shall preferably not be a line scan camera with only one image line, i.e. several pixels adjacent to each other in one row forming one image line.
In the proposed inspection method the images captured are forwarded to an image analysis unit, which is configured to ascertain surface anomalies as defect areas in a detection, in particular by means of image evaluation programs. To this end the image evaluation programs comprise suitable algorithms for evaluating individual pixels or pixel areas of, for example, the intensity values of the color sensors assigned to the individual pixels. The color sensors may be one sensor for a monochrome image or several sensors for a color image. As required, in the image analysis unit, the defect areas may also be delimited in a segmentation in particular by means of image evaluation programs, in relation to each other or against the image background, defect areas belonging together are summarized in a region analysis in particular by means of image evaluation programs, and/or characteristic defect features are derived from defect areas or defect regions in a feature extraction in particular by means of image evaluation programs, which then are or may be used for a subsequent defect classification.
With the invention the area image sensor is calibrated three-dimensionally onto a selected coordinate system. “Three-dimensionally calibrated” means that the arrangement, i.e. the position and the orientation of the area image sensor as well as its imaging properties in e.g. a camera model, are known, so that, by utilizing suitable features known in the selected coordinate system and/or in another coordinate system, which are mapped in the picture taken, it is possible to determine the position of these features and/or the captured object examined or to be examined. For example these features may be characteristic features of the object, which are known in an object coordinate system. Such image evaluation methods are generally known and therefore need not be described here in detail.
When carrying out the method proposed according to the invention the object examined or to be examined is moved with its surface relative to the preferably stationary area image sensor in the selected coordinate system. The selected coordinate system may for example be the world coordinate system of the environment.
The invention thus relates to systems for a high-resolution, comprehensive and contactless capture of the form properties and reflective properties of objects in conjunction with methods for data fusion and data analysis for the inspection of medium-area or large-area surfaces in the material-production or material-processing industry. Such systems permit the capture of reflective properties of surfaces in real time and can be used as surface inspection systems for surface quality inspection as well as for process analysis and process control.
The known systems for a contactless inspection of medium-area or large-area surfaces are limited to the sensory capture of the reflective properties of the surface and for this purpose make use of two-dimensional scanning of the surface using an image sensor, such as a CCD element with an imaging optics which maps the image area to be captured onto the CCD element. These image sensors are also called cameras.
The reflective properties are measured with the aid of a two-dimensional image of the surface, which is captured by black-and-white or color cameras with monochromatic or polychromatic (colored) illumination and, as required, using suitable optical filters. The cameras used may be area-scan or line-scan cameras in conjunction with appropriate illuminating units. The illumination and viewing angles as well as the used illumination wavelengths (monochromatic or polychromatic) are adapted to suit the respective inspection task. The image sensor used supplies a continuous data stream of single- or multi-channel images, wherein the image information (in particular intensity values for one or different wavelengths) is available as single- or multi-channel information for each pixel or each pixel area of the area-scan camera.
To locate, evaluate and categorize surface defects the captured image (grey-scale image or color image) is subjected to an automatic image analysis, during which image analysis algorithms initially discover surface anomalies (detection), delimit the defect areas in relation to each other and to the background (segmentation) and, as required, summarize defect areas belonging together (region analysis). In order to categorizes and evaluate the recognized surface anomalies into application and surface-specific defect classes, characteristic image features are derived from the image function within the defect regions (feature extraction). Then, in a classification process each found conspicuity is assigned a defect category and, depending on the respective application, a degree of severity. Conspicuities not evaluated as surface defects, are classified as so-called “pseudo defects”.
The information content of a two-dimensional image of the surface to be examined, as obtained by the known systems for surface inspection, is however limited. Certain surface properties are simply not suitable for measuring reflection properties. Also intensity or color information, even for an optimal sensor construction and an optimal data evaluation, do not supply sufficient evidence for a reliable separation between flawed and flawless surface structures. For example, for extremely heavily structured surfaces, such as slab surfaces, a large number of pseudo defects is frequently observed independently of the detection process as such, since the generally uncritical material structures show up as en-masse conspicuities in a color or intensity image, which are detected as surface anomalies and thus as defect areas.
With the known inspection processes therefore conspicuities show up all over the material surface, when in effect these are not surface defects but commonly known material structures. On the other hand relevant surface anomalies such as cracks or indentations are difficult to distinguish from these pseudo defects by way of the intensity or color picture. For the downstream classification process therefore it is possible only with a great deal of uncertainty to differentiate between actual defects and pseudo-defects, leading, depending on the inspection task, to a considerable amount of false alarms which results in a considerable amount of manual reworking.